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Decoding images from brain activity has been a challenge. Owing to thedevelopment of deep learning, there are available tools to solve this problem.The decoded image, which aims to map neural spike trains to low-level visualfeatures and high-level semantic information space. Recently, there are a fewstudies of decoding from spike trains, however, these studies pay lessattention to the foundations of neuroscience and there are few studies thatmerged receptive field into visual image reconstruction. In this paper, wepropose a deep learning neural network architecture with biological propertiesto reconstruct visual image from spike trains. As far as we know, weimplemented a method that integrated receptive field property matrix into lossfunction at the first time. Our model is an end-to-end decoder from neuralspike trains to images. We not only merged Gabor filter into auto-encoder whichused to generate images but also proposed a loss function with receptive fieldproperties. We evaluated our decoder on two datasets which contain macaqueprimary visual cortex neural spikes and salamander retina ganglion cells (RGCs)spikes. Our results show that our method can effectively combine receptivefield features to reconstruct images, providing a new approach to visualreconstruction based on neural information.

The Brain-Inspired Decoder for Natural Visual Image Reconstruction